Update app.py

app.py

@@ -3,27 +3,6 @@
 
 # #### Gradio Comparing Transfer Learning Models
 
-# In[1]:
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-import tensorflow as tf
-print(tf.__version__)
-
-
-# In[2]:
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-pip install gradio
-
-
-# In[3]:
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-pip install MarkupSafe
-
-
-# In[1]:
-
 
 import gradio as gr
 import tensorflow as tf
@@ -93,154 +72,5 @@ gr.Interface(
     examples=sample_images).launch()
 
 
-# In[6]:
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-pip install transformers
-
-
-# In[6]:
-
-
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-# Load the models and tokenizers
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-tokenizer1 = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-imdb")
-tokenizer2 = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
-model1 = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-imdb")
-model2 = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
-
-
-
-
-# Define the sentiment prediction functions
-def predict_sentiment(text):
-    # Predict sentiment using model 1
-    inputs1 = tokenizer1.encode_plus(text, padding="longest", truncation=True, return_tensors="pt")
-    outputs1 = model1(**inputs1)
-    predicted_label1 = outputs1.logits.argmax().item()
-    sentiment1 = "Positive" if predicted_label1 == 1 else "Negative" if predicted_label1 == 0 else "Neutral"
-
-    # Predict sentiment using model 2
-    inputs2 = tokenizer2.encode_plus(text, padding="longest", truncation=True, return_tensors="pt")
-    outputs2 = model2(**inputs2)
-    predicted_label2 = outputs2.logits.argmax().item()
-    sentiment2 = "Positive" if predicted_label2 == 1 else "Negative" if predicted_label2 == 0 else "Neutral"
-
-    return sentiment1, sentiment2
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict_sentiment,
-    inputs="text",
-    outputs=["text", "text"],
-    title="Sentiment Analysis (Model 1 vs Model 2)",
-    description="Compare sentiment predictions from two models.",
-)
-
-# Launch the interface
-iface.launch()
-
-
-# In[17]:
-
-
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-from torchvision import transforms
-from io import BytesIO
-from PIL import Image
-
-# Define the available models and datasets
-models = {
-    "Model 1": {
-        "model_name": "bert-base-uncased",
-        "tokenizer": None,
-        "model": None
-    },
-    "Model 2": {
-        "model_name": "distilbert-base-uncased",
-        "tokenizer": None,
-        "model": None
-    },
-    # Add more models as needed
-}
-
-datasets = {
-    "Dataset 1": {
-        "name": "imdb",
-        "split": "test",
-        "features": ["text"],
-    },
-    "Dataset 2": {
-        "name": "ag_news",
-        "split": "test",
-        "features": ["text"],
-    },
-    # Add more datasets as needed
-}
-
-# Load models
-for model_key, model_info in models.items():
-    tokenizer = AutoTokenizer.from_pretrained(model_info["model_name"])
-    model = AutoModelForSequenceClassification.from_pretrained(model_info["model_name"])
-    model_info["tokenizer"] = tokenizer
-    model_info["model"] = model
-
-# Set the device to GPU if available
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-for model_info in models.values():
-    model_info["model"].to(device)
-
-# Define the preprocessing function
-def preprocess(image_file):
-    image = Image.open(BytesIO(image_file.read())).convert("RGB")
-    preprocess_transform = transforms.Compose([
-        transforms.Resize((224, 224)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    ])
-    image = preprocess_transform(image)
-    image = image.unsqueeze(0)
-    return image.to(device)
-
-# Define the prediction function
-def predict(image_file, model_key):
-    model_info = models[model_key]
-    tokenizer = model_info["tokenizer"]
-    model = model_info["model"]
-
-    image = preprocess(image_file)
-
-    with torch.no_grad():
-        outputs = model(image)
-
-    predictions = outputs.logits.argmax(dim=1)
-
-    return predictions.item()
-
-def classify_image(image, model_key):
-    image = Image.fromarray(image.astype('uint8'), 'RGB')
-    image_file = BytesIO()
-    image.save(image_file, format="JPEG")
-    prediction = predict(image_file=image_file, model_key=model_key)
-    return prediction
-
-iface = gr.Interface(fn=classify_image,
-                     inputs=["image", gr.inputs.Dropdown(list(models.keys()), label="Model")],
-                     outputs="text",
-                     title="Image Classification",
-                     description="Classify images using Hugging Face models")
-
-iface.launch()
-
-
-# In[ ]:
-
-